Uncertainty-inspired open set learning for retinal anomaly identification

Uncertainty-inspired open set learning for retinal anomaly identification

Abstract Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with f...

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Bibliographic Details
Main Authors: Meng Wang, Tian Lin, Lianyu Wang, Aidi Lin, Ke Zou, Xinxing Xu, Yi Zhou, Yuanyuan Peng, Qingquan Meng, Yiming Qian, Guoyao Deng, Zhiqun Wu, Junhong Chen, Jianhong Lin, Mingzhi Zhang, Weifang Zhu, Changqing Zhang, Daoqiang Zhang, Rick Siow Mong Goh, Yong Liu, Chi Pui Pang, Xinjian Chen, Haoyu Chen, Huazhu Fu
Format: Article
Language:English
Published: Nature Portfolio 2023-10-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-023-42444-7

Internet

https://doi.org/10.1038/s41467-023-42444-7

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